'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   @property
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			str:
        torch.manual_seed(0     )
        _A    		=  UNetaDModel(
            block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=3, out_channels=3, down_block_types=('DownBlock2D', 'AttnDownBlock2D'), up_block_types=('AttnUpBlock2D', 'UpBlock2D'), )
        return model

   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Union[str, Any]:
        _A    		=  self.dummy_uncond_unet
        _A    		=  ScoreSdeVeScheduler()

        _A    		=  ScoreSdeVePipeline(unet=UpperCamelCase__, scheduler=UpperCamelCase__     )
        sde_ve.to(UpperCamelCase__     )
        sde_ve.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  torch.manual_seed(0     )
        _A    		=  sde_ve(num_inference_steps=2, output_type='numpy', generator=UpperCamelCase__     ).images

        _A    		=  torch.manual_seed(0     )
        _A    		=  sde_ve(num_inference_steps=2, output_type='numpy', generator=UpperCamelCase__, return_dict=UpperCamelCase__     )[
            0
        ]

        _A    		=  image[0, -3:, -3:, -1]
        _A    		=  image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        _A    		=  np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]     )

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1e-2







@slow
@require_torch
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			List[str]:
        _A    		=  'google/ncsnpp-church-256'
        _A    		=  UNetaDModel.from_pretrained(UpperCamelCase__     )

        _A    		=  ScoreSdeVeScheduler.from_pretrained(UpperCamelCase__     )

        _A    		=  ScoreSdeVePipeline(unet=UpperCamelCase__, scheduler=UpperCamelCase__     )
        sde_ve.to(UpperCamelCase__     )
        sde_ve.set_progress_bar_config(disable=UpperCamelCase__     )

        _A    		=  torch.manual_seed(0     )
        _A    		=  sde_ve(num_inference_steps=10, output_type='numpy', generator=UpperCamelCase__     ).images

        _A    		=  image[0, -3:, -3:, -1]

        assert image.shape == (1, 2_56, 2_56, 3)

        _A    		=  np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]     )

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2











"""simple docstring"""





def 	lowerCamelCase__			(				_lowerCamelCase			:						float						,							_lowerCamelCase			:						float					) ->							float:

  if density <= 0:
    raise ValueError('Impossible fluid density'					)
  if bulk_modulus <= 0:
    raise ValueError('Impossible bulk modulus'					)

  return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
  import doctest

  doctest.testmod()




